



"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_UpperCAmelCase     =			logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					str       ,		lowerCAmelCase :					AutoencoderKL       ,		lowerCAmelCase :					CLIPTextModel       ,		lowerCAmelCase :					CLIPTokenizer       ,		lowerCAmelCase :					UNetaDConditionModel       ,		lowerCAmelCase :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,		lowerCAmelCase :					StableDiffusionSafetyChecker       ,		lowerCAmelCase :					CLIPImageProcessor       ,		)     ->					str:


     '''simple docstring'''




     super().__init__()
     self.register_modules(
         vae=lowerCAmelCase       ,		text_encoder=lowerCAmelCase       ,		tokenizer=lowerCAmelCase       ,		unet=lowerCAmelCase       ,		scheduler=lowerCAmelCase       ,		safety_checker=lowerCAmelCase       ,		feature_extractor=lowerCAmelCase       ,		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[Union[str, int]] = "auto"		)     ->					Tuple:


     '''simple docstring'''




     if slice_size == "auto":
        # half the attention head size is usually a good trade-off between
        # speed and memory
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.unet.config.attention_head_dim // 2
     self.unet.set_attention_slice(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[str]:


     '''simple docstring'''




     self.enable_attention_slicing(lowerCAmelCase		)





  @torch.no_grad()
  def __call__(						self :					Optional[int]       ,		lowerCAmelCase :					Union[str, List[str]]       ,		lowerCAmelCase :					int = 512       ,		lowerCAmelCase :					int = 512       ,		lowerCAmelCase :					int = 50       ,		lowerCAmelCase :					float = 7.5       ,		lowerCAmelCase :					Optional[Union[str, List[str]]] = None       ,		lowerCAmelCase :					Optional[int] = 1       ,		lowerCAmelCase :					float = 0.0       ,		lowerCAmelCase :					Optional[torch.Generator] = None       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		lowerCAmelCase :					Optional[str] = "pil"       ,		lowerCAmelCase :					bool = True       ,		lowerCAmelCase :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,		lowerCAmelCase :					int = 1       ,		lowerCAmelCase :					Optional[torch.FloatTensor] = None       ,		**lowerCAmelCase :					Tuple       ,		)     ->					Optional[Any]:


     '''simple docstring'''




     if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: int     			=1
     elif isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
        SCREAMING_SNAKE_CASE_: str     			=len(lowerCAmelCase		)
     else:
        raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase		)}'''		)

     if height % 8 != 0 or width % 8 != 0:
        raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''		)

     if (callback_steps is None) or (
         callback_steps is not None and (not isinstance(lowerCAmelCase       ,		lowerCAmelCase		) or callback_steps <= 0)
     ):
        raise ValueError(
            f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
            f''' {type(lowerCAmelCase		)}.'''		)

     # get prompt text embeddings
     SCREAMING_SNAKE_CASE_: int     			=self.tokenizer(
         lowerCAmelCase       ,		padding="""max_length"""       ,		max_length=self.tokenizer.model_max_length       ,		return_tensors="""pt"""       ,		)
     SCREAMING_SNAKE_CASE_: List[str]     			=text_inputs.input_ids

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
        logger.warning(
            """The following part of your input was truncated because CLIP can only handle sequences up to"""
            f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=text_input_ids[:, : self.tokenizer.model_max_length]

     if text_embeddings is None:
        SCREAMING_SNAKE_CASE_: List[str]     			=self.text_encoder(text_input_ids.to(self.device		)		)[0]

     # duplicate text embeddings for each generation per prompt, using mps friendly method
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=text_embeddings.shape
     SCREAMING_SNAKE_CASE_: Any     			=text_embeddings.repeat(1       ,		lowerCAmelCase       ,		1		)
     SCREAMING_SNAKE_CASE_: Any     			=text_embeddings.view(bs_embed * num_images_per_prompt       ,		lowerCAmelCase       ,		-1		)

     # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
     # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
     # corresponds to doing no classifier free guidance.
     SCREAMING_SNAKE_CASE_: int     			=guidance_scale > 1.0
     # get unconditional embeddings for classifier free guidance
     if do_classifier_free_guidance:
        SCREAMING_SNAKE_CASE_: List[str]
        if negative_prompt is None:
           SCREAMING_SNAKE_CASE_: Union[str, Any]     			=[""""""]
        elif type(lowerCAmelCase		) is not type(lowerCAmelCase		):
           raise TypeError(
               f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase		)} !='''
               f''' {type(lowerCAmelCase		)}.'''		)
        elif isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           SCREAMING_SNAKE_CASE_: Any     			=[negative_prompt]
        elif batch_size != len(lowerCAmelCase		):
           raise ValueError(
               f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase		)}, but `prompt`:'''
               f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
               """ the batch size of `prompt`."""		)
        else:
           SCREAMING_SNAKE_CASE_: Optional[int]     			=negative_prompt

        SCREAMING_SNAKE_CASE_: List[Any]     			=text_input_ids.shape[-1]
        SCREAMING_SNAKE_CASE_: Tuple     			=self.tokenizer(
            lowerCAmelCase       ,		padding="""max_length"""       ,		max_length=lowerCAmelCase       ,		truncation=lowerCAmelCase       ,		return_tensors="""pt"""       ,		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=uncond_embeddings.shape[1]
        SCREAMING_SNAKE_CASE_: Tuple     			=uncond_embeddings.repeat(lowerCAmelCase       ,		lowerCAmelCase       ,		1		)
        SCREAMING_SNAKE_CASE_: Tuple     			=uncond_embeddings.view(batch_size * num_images_per_prompt       ,		lowerCAmelCase       ,		-1		)

        # For classifier free guidance, we need to do two forward passes.
        # Here we concatenate the unconditional and text embeddings into a single batch
        # to avoid doing two forward passes
        SCREAMING_SNAKE_CASE_: Tuple     			=torch.cat([uncond_embeddings, text_embeddings]		)

     # get the initial random noise unless the user supplied it

     # Unlike in other pipelines, latents need to be generated in the target device
     # for 1-to-1 results reproducibility with the CompVis implementation.
     # However this currently doesn't work in `mps`.
     SCREAMING_SNAKE_CASE_: Tuple     			=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
     SCREAMING_SNAKE_CASE_: int     			=(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
     SCREAMING_SNAKE_CASE_: str     			=text_embeddings.dtype
     if latents is None:
        if self.device.type == "mps":
           # randn does not exist on mps
           SCREAMING_SNAKE_CASE_: int     			=torch.randn(
               lowerCAmelCase       ,		generator=lowerCAmelCase       ,		device="""cpu"""       ,		dtype=lowerCAmelCase		).to(self.device		)
           SCREAMING_SNAKE_CASE_: Union[str, Any]     			=torch.randn(lowerCAmelCase       ,		generator=lowerCAmelCase       ,		device="""cpu"""       ,		dtype=lowerCAmelCase		).to(
               self.device		)
        else:
           SCREAMING_SNAKE_CASE_: List[str]     			=torch.randn(
               lowerCAmelCase       ,		generator=lowerCAmelCase       ,		device=self.device       ,		dtype=lowerCAmelCase		)
           SCREAMING_SNAKE_CASE_: List[str]     			=torch.randn(lowerCAmelCase       ,		generator=lowerCAmelCase       ,		device=self.device       ,		dtype=lowerCAmelCase		)
     else:
        if latents_reference.shape != latents_shape:
           raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''		)
        SCREAMING_SNAKE_CASE_: List[str]     			=latents_reference.to(self.device		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=latents.to(self.device		)

     # This is the key part of the pipeline where we
     # try to ensure that the generated images w/ the same seed
     # but different sizes actually result in similar images
     SCREAMING_SNAKE_CASE_: int     			=(latents_shape[3] - latents_shape_reference[3]) // 2
     SCREAMING_SNAKE_CASE_: Optional[int]     			=(latents_shape[2] - latents_shape_reference[2]) // 2
     SCREAMING_SNAKE_CASE_: List[Any]     			=latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
     SCREAMING_SNAKE_CASE_: Any     			=0 if dx < 0 else dx
     SCREAMING_SNAKE_CASE_: Tuple     			=0 if dy < 0 else dy
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=max(-dx       ,		0		)
     SCREAMING_SNAKE_CASE_: int     			=max(-dy       ,		0		)
     # import pdb
     # pdb.set_trace()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=latents_reference[:, :, dy : dy + h, dx : dx + w]

     # set timesteps
     self.scheduler.set_timesteps(lowerCAmelCase		)

     # Some schedulers like PNDM have timesteps as arrays
     # It's more optimized to move all timesteps to correct device beforehand
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.scheduler.timesteps.to(self.device		)

     # scale the initial noise by the standard deviation required by the scheduler
     SCREAMING_SNAKE_CASE_: Tuple     			=latents * self.scheduler.init_noise_sigma

     # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
     # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
     # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
     # and should be between [0, 1]
     SCREAMING_SNAKE_CASE_: Dict     			="""eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
     SCREAMING_SNAKE_CASE_: Any     			={}
     if accepts_eta:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=eta

     for i, t in enumerate(self.progress_bar(lowerCAmelCase		)		):
        # expand the latents if we are doing classifier free guidance
        SCREAMING_SNAKE_CASE_: Tuple     			=torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.scheduler.scale_model_input(lowerCAmelCase       ,		lowerCAmelCase		)

        # predict the noise residual
        SCREAMING_SNAKE_CASE_: Any     			=self.unet(lowerCAmelCase       ,		lowerCAmelCase       ,		encoder_hidden_states=lowerCAmelCase		).sample

        # perform guidance
        if do_classifier_free_guidance:
           SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=noise_pred.chunk(2		)
           SCREAMING_SNAKE_CASE_: int     			=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

        # compute the previous noisy sample x_t -> x_t-1
        SCREAMING_SNAKE_CASE_: Dict     			=self.scheduler.step(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase       ,		**lowerCAmelCase		).prev_sample

        # call the callback, if provided
        if callback is not None and i % callback_steps == 0:
           callback(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=1 / 0.1_8_2_1_5 * latents
     SCREAMING_SNAKE_CASE_: Dict     			=self.vae.decode(lowerCAmelCase		).sample

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=(image / 2 + 0.5).clamp(0       ,		1		)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image.cpu().permute(0       ,		2       ,		3       ,		1		).float().numpy()

     if self.safety_checker is not None:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=self.feature_extractor(self.numpy_to_pil(lowerCAmelCase		)       ,		return_tensors="""pt"""		).to(
            self.device		)
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=self.safety_checker(
            images=lowerCAmelCase       ,		clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
     else:
        SCREAMING_SNAKE_CASE_: int     			=None

     if output_type == "pil":
        SCREAMING_SNAKE_CASE_: Tuple     			=self.numpy_to_pil(lowerCAmelCase		)

     if not return_dict:
        return (image, has_nsfw_concept)

     return StableDiffusionPipelineOutput(images=lowerCAmelCase       ,		nsfw_content_detected=lowerCAmelCase		)








"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: 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 ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

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

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

   if repo_id:
      print("""Pushing to the hub...""" )
      model.push_to_hub(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			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."""
   )

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



