


import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


__lowerCamelCase        =     logging.get_logger(__name__)

__lowerCamelCase        =     {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}


class   a__	:

       def __init__(   self      :     str      ,							lowerCamelCase_      :     List[str]=None      ,							**lowerCamelCase_      :     Tuple    ):
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""    )
            a_    :     Tuple										=    model
            a_    :     Tuple										=    kwargs.get("""model_save_dir"""      ,							lowerCamelCase_    )
            a_    :     List[str]										=    kwargs.get("""latest_model_name"""      ,							lowerCamelCase_    )

       def __call__(   self      :     Optional[int]      ,							**lowerCamelCase_      :     Any    ):
            a_    :     Any										=    {k: np.array(lowerCamelCase_    ) for k, v in kwargs.items()}
            return self.model.run(lowerCamelCase_      ,							lowerCamelCase_    )

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     Union[str, Path]      ,							lowerCamelCase_      :     Any=None      ,							lowerCamelCase_      :     Union[str, Any]=None    ):
            if provider is None:
                 logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""    )
                 a_    :     List[Any]										=    """CPUExecutionProvider"""

            return ort.InferenceSession(lowerCamelCase_      ,							providers=[provider]      ,							sess_options=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str      ,							lowerCamelCase_      :     Union[str, Path]      ,							lowerCamelCase_      :     Optional[str] = None      ,							**lowerCamelCase_      :     List[str]    ):
            a_    :     Optional[int]										=    file_name if file_name is not None else ONNX_WEIGHTS_NAME

            a_    :     Optional[int]										=    self.model_save_dir.joinpath(self.latest_model_name    )
            a_    :     Optional[Any]										=    Path(lowerCamelCase_    ).joinpath(lowerCamelCase_    )
            try:
                 shutil.copyfile(lowerCamelCase_      ,							lowerCamelCase_    )
            except shutil.SameFileError:
                 pass

            # copy external weights (for models >2GB)
            a_    :     Tuple										=    self.model_save_dir.joinpath(lowerCamelCase_    )
            if src_path.exists():
                 a_    :     List[str]										=    Path(lowerCamelCase_    ).joinpath(lowerCamelCase_    )
                 try:
                      shutil.copyfile(lowerCamelCase_      ,							lowerCamelCase_    )
                 except shutil.SameFileError:
                      pass

       def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     Union[str, os.PathLike]      ,							**lowerCamelCase_      :     Dict      ,							):
            if os.path.isfile(lowerCamelCase_    ):
                 logger.error(F'''Provided path ({save_directory}) should be a directory, not a file'''    )
                 return

            os.makedirs(lowerCamelCase_      ,							exist_ok=lowerCamelCase_    )

            # saving model weights/files
            self._save_pretrained(lowerCamelCase_      ,							**lowerCamelCase_    )

       @classmethod
       def 						UpperCAmelCase(   cls      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Path]      ,							lowerCamelCase_      :     Optional[Union[bool, str, None]] = None      ,							lowerCamelCase_      :     Optional[Union[str, None]] = None      ,							lowerCamelCase_      :     bool = False      ,							lowerCamelCase_      :     Optional[str] = None      ,							lowerCamelCase_      :     Optional[str] = None      ,							lowerCamelCase_      :     Optional[str] = None      ,							lowerCamelCase_      :     Optional["ort.SessionOptions"] = None      ,							**lowerCamelCase_      :     List[Any]      ,							):
            a_    :     Optional[int]										=    file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowerCamelCase_    ):
                 a_    :     Union[str, Any]										=    OnnxRuntimeModel.load_model(
                     os.path.join(lowerCamelCase_      ,							lowerCamelCase_    )      ,							provider=lowerCamelCase_      ,							sess_options=lowerCamelCase_    )
                 a_    :     Dict										=    Path(lowerCamelCase_    )
            # load model from hub
            else:
                 # download model
                 a_    :     Tuple										=    hf_hub_download(
                     repo_id=lowerCamelCase_      ,							filename=lowerCamelCase_      ,							use_auth_token=lowerCamelCase_      ,							revision=lowerCamelCase_      ,							cache_dir=lowerCamelCase_      ,							force_download=lowerCamelCase_      ,							)
                 a_    :     int										=    Path(lowerCamelCase_    ).parent
                 a_    :     Any										=    Path(lowerCamelCase_    ).name
                 a_    :     Union[str, Any]										=    OnnxRuntimeModel.load_model(lowerCamelCase_      ,							provider=lowerCamelCase_      ,							sess_options=lowerCamelCase_    )
            return cls(model=lowerCamelCase_      ,							**lowerCamelCase_    )

       @classmethod
       def 						UpperCAmelCase(   cls      :     Tuple      ,							lowerCamelCase_      :     Union[str, Path]      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[str] = None      ,							lowerCamelCase_      :     Optional[str] = None      ,							**lowerCamelCase_      :     List[Any]      ,							):
            a_    :     str										=    None
            if len(str(lowerCamelCase_    ).split("""@"""    )    ) == 2:
                 a_			,			a_    :     Optional[int]										=    model_id.split("""@"""    )

            return cls._from_pretrained(
                model_id=lowerCamelCase_      ,							revision=lowerCamelCase_      ,							cache_dir=lowerCamelCase_      ,							force_download=lowerCamelCase_      ,							use_auth_token=lowerCamelCase_      ,							**lowerCamelCase_      ,							)







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel


class   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     List[str]							    =     ["""vqvae"""]

       def __init__(   self      :     str      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Mel      ,							lowerCamelCase_      :     Union[DDIMScheduler, DDPMScheduler]      ,							):
            super().__init__()
            self.register_modules(unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							mel=lowerCamelCase_      ,							vqvae=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            return 5_0 if isinstance(self.scheduler      ,							lowerCamelCase_    ) else 1_0_0_0

       @torch.no_grad()
       def __call__(   self      :     List[str]      ,							lowerCamelCase_      :     int = 1      ,							lowerCamelCase_      :     str = None      ,							lowerCamelCase_      :     np.ndarray = None      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     int = None      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Generator = None      ,							lowerCamelCase_      :     float = 0      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     torch.Tensor = None      ,							lowerCamelCase_      :     List[Any]=True      ,							):
            a_    :     Optional[Any]										=    steps or self.get_default_steps()
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Optional[int]										=    step_generator or generator
            # For backwards compatibility
            if type(self.unet.config.sample_size    ) == int:
                 a_    :     str										=    (self.unet.config.sample_size, self.unet.config.sample_size)
            if noise is None:
                 a_    :     Dict										=    randn_tensor(
                     (
                         batch_size,
                         self.unet.config.in_channels,
                         self.unet.config.sample_size[0],
                         self.unet.config.sample_size[1],
                     )      ,							generator=lowerCamelCase_      ,							device=self.device      ,							)
            a_    :     Tuple										=    noise
            a_    :     Union[str, Any]										=    None

            if audio_file is not None or raw_audio is not None:
                 self.mel.load_audio(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    self.mel.audio_slice_to_image(lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    np.frombuffer(input_image.tobytes()      ,							dtype="""uint8"""    ).reshape(
                     (input_image.height, input_image.width)    )
                 a_    :     Union[str, Any]										=    (input_image / 2_5_5) * 2 - 1
                 a_    :     Tuple										=    torch.tensor(input_image[np.newaxis, :, :]      ,							dtype=torch.float    ).to(self.device    )

                 if self.vqvae is not None:
                      a_    :     Dict										=    self.vqvae.encode(torch.unsqueeze(lowerCamelCase_      ,							0    )    ).latent_dist.sample(
                          generator=lowerCamelCase_    )[0]
                      a_    :     List[Any]										=    self.vqvae.config.scaling_factor * input_images

                 if start_step > 0:
                      a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							self.scheduler.timesteps[start_step - 1]    )

                 a_    :     Union[str, Any]										=    (
                     self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
                 )
                 a_    :     int										=    int(mask_start_secs * pixels_per_second    )
                 a_    :     Tuple										=    int(mask_end_secs * pixels_per_second    )
                 a_    :     Tuple										=    self.scheduler.add_noise(lowerCamelCase_      ,							lowerCamelCase_      ,							torch.tensor(self.scheduler.timesteps[start_step:]    )    )

            for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]    )    ):
                 if isinstance(self.unet      ,							lowerCamelCase_    ):
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 else:
                      a_    :     Dict										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]

                 if isinstance(self.scheduler      ,							lowerCamelCase_    ):
                      a_    :     Any										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]
                 else:
                      a_    :     Optional[Any]										=    self.scheduler.step(
                          model_output=lowerCamelCase_      ,							timestep=lowerCamelCase_      ,							sample=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							)["""prev_sample"""]

                 if mask is not None:
                      if mask_start > 0:
                           a_    :     Any										=    mask[:, step, :, :mask_start]
                      if mask_end > 0:
                           a_    :     Dict										=    mask[:, step, :, -mask_end:]

            if self.vqvae is not None:
                 # 0.18215 was scaling factor used in training to ensure unit variance
                 a_    :     str										=    1 / self.vqvae.config.scaling_factor * images
                 a_    :     List[Any]										=    self.vqvae.decode(lowerCamelCase_    )["""sample"""]

            a_    :     Union[str, Any]										=    (images / 2 + 0.5).clamp(0      ,							1    )
            a_    :     Dict										=    images.cpu().permute(0      ,							2      ,							3      ,							1    ).numpy()
            a_    :     Any										=    (images * 2_5_5).round().astype("""uint8"""    )
            a_    :     int										=    list(
                (Image.fromarray(_[:, :, 0]    ) for _ in images)
                if images.shape[3] == 1
                else (Image.fromarray(lowerCamelCase_      ,							mode="""RGB"""    ).convert("""L"""    ) for _ in images)    )

            a_    :     str										=    [self.mel.image_to_audio(lowerCamelCase_    ) for _ in images]
            if not return_dict:
                 return images, (self.mel.get_sample_rate(), audios)

            return BaseOutput(**AudioPipelineOutput(np.array(lowerCamelCase_    )[:, np.newaxis, :]    )      ,							**ImagePipelineOutput(lowerCamelCase_    )    )

       @torch.no_grad()
       def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     List[Image.Image]      ,							lowerCamelCase_      :     int = 5_0    ):
            assert isinstance(self.scheduler      ,							lowerCamelCase_    )
            self.scheduler.set_timesteps(lowerCamelCase_    )
            a_    :     Any										=    np.array(
                [np.frombuffer(image.tobytes()      ,							dtype="""uint8"""    ).reshape((1, image.height, image.width)    ) for image in images]    )
            a_    :     Union[str, Any]										=    (sample / 2_5_5) * 2 - 1
            a_    :     Optional[Any]										=    torch.Tensor(lowerCamelCase_    ).to(self.device    )

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps      ,							(0,)    )    ):
                 a_    :     str										=    t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 a_    :     Any										=    self.scheduler.alphas_cumprod[t]
                 a_    :     Dict										=    (
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 a_    :     Optional[Any]										=    1 - alpha_prod_t
                 a_    :     Optional[int]										=    self.unet(lowerCamelCase_      ,							lowerCamelCase_    )["""sample"""]
                 a_    :     Tuple										=    (1 - alpha_prod_t_prev) ** 0.5 * model_output
                 a_    :     Tuple										=    (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 a_    :     Tuple										=    sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     torch.Tensor      ,							lowerCamelCase_      :     float    ):
            a_    :     Dict										=    acos(torch.dot(torch.flatten(lowerCamelCase_    )      ,							torch.flatten(lowerCamelCase_    )    ) / torch.norm(lowerCamelCase_    ) / torch.norm(lowerCamelCase_    )    )
            return sin((1 - alpha) * theta    ) * xa / sin(lowerCamelCase_    ) + sin(alpha * theta    ) * xa / sin(lowerCamelCase_    )



