

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 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      ["vqvae"]





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





   def      _snake_case		(       self					:			int			):
       return 50 if isinstance(self.scheduler     , __lowerCamelCase			) else 1000





   @torch.no_grad()
   def __call__(       self					:			Dict     , __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					:			Optional[int]=True     , ):
       SCREAMING_SNAKE_CASE										=   steps or self.get_default_steps()
       self.scheduler.set_timesteps(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size			) == int:
           SCREAMING_SNAKE_CASE										=   (self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
           SCREAMING_SNAKE_CASE										=   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     , )
       SCREAMING_SNAKE_CASE										=   noise
       SCREAMING_SNAKE_CASE										=   None

       if audio_file is not None or raw_audio is not None:
           self.mel.load_audio(__lowerCamelCase     , __lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   self.mel.audio_slice_to_image(__lowerCamelCase			)
           SCREAMING_SNAKE_CASE										=   np.frombuffer(input_image.tobytes()     , dtype="uint8"			).reshape(
               (input_image.height, input_image.width)			)
           SCREAMING_SNAKE_CASE										=   (input_image / 255) * 2 - 1
           SCREAMING_SNAKE_CASE										=   torch.tensor(input_image[np.newaxis, :, :]     , dtype=torch.float			).to(self.device			)

           if self.vqvae is not None:
               SCREAMING_SNAKE_CASE										=   self.vqvae.encode(torch.unsqueeze(__lowerCamelCase     , 0			)			).latent_dist.sample(
                   generator=__lowerCamelCase			)[0]
               SCREAMING_SNAKE_CASE										=   self.vqvae.config.scaling_factor * input_images

           if start_step > 0:
               SCREAMING_SNAKE_CASE										=   self.scheduler.add_noise(__lowerCamelCase     , __lowerCamelCase     , self.scheduler.timesteps[start_step - 1]			)

           SCREAMING_SNAKE_CASE										=   (
               self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
           )
           SCREAMING_SNAKE_CASE										=   int(mask_start_secs * pixels_per_second			)
           SCREAMING_SNAKE_CASE										=   int(mask_end_secs * pixels_per_second			)
           SCREAMING_SNAKE_CASE										=   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			):
               SCREAMING_SNAKE_CASE										=   self.unet(__lowerCamelCase     , __lowerCamelCase     , __lowerCamelCase			)["sample"]
           else:
               SCREAMING_SNAKE_CASE										=   self.unet(__lowerCamelCase     , __lowerCamelCase			)["sample"]

           if isinstance(self.scheduler     , __lowerCamelCase			):
               SCREAMING_SNAKE_CASE										=   self.scheduler.step(
                   model_output=__lowerCamelCase     , timestep=__lowerCamelCase     , sample=__lowerCamelCase     , eta=__lowerCamelCase     , generator=__lowerCamelCase     , )["prev_sample"]
           else:
               SCREAMING_SNAKE_CASE										=   self.scheduler.step(
                   model_output=__lowerCamelCase     , timestep=__lowerCamelCase     , sample=__lowerCamelCase     , generator=__lowerCamelCase     , )["prev_sample"]

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

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

       SCREAMING_SNAKE_CASE										=   (images / 2 + 0.5).clamp(0     , 1			)
       SCREAMING_SNAKE_CASE										=   images.cpu().permute(0     , 2     , 3     , 1			).numpy()
       SCREAMING_SNAKE_CASE										=   (images * 255).round().astype("uint8"			)
       SCREAMING_SNAKE_CASE										=   list(
           (Image.fromarray(_[:, :, 0]			) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(__lowerCamelCase     , mode="RGB"			).convert("L"			) for _ in images)			)

       SCREAMING_SNAKE_CASE										=   [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      _snake_case		(       self					:			int     , __lowerCamelCase					:			List[Image.Image]     , __lowerCamelCase					:			int = 50			):
       assert isinstance(self.scheduler     , __lowerCamelCase			)
       self.scheduler.set_timesteps(__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   np.array(
           [np.frombuffer(image.tobytes()     , dtype="uint8"			).reshape((1, image.height, image.width)			) for image in images]			)
       SCREAMING_SNAKE_CASE										=   (sample / 255) * 2 - 1
       SCREAMING_SNAKE_CASE										=   torch.Tensor(__lowerCamelCase			).to(self.device			)

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

       return sample





   @staticmethod
   def      _snake_case		(       __lowerCamelCase					:			torch.Tensor     , __lowerCamelCase					:			torch.Tensor     , __lowerCamelCase					:			float			):
       SCREAMING_SNAKE_CASE										=   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			)


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					Optional[Any]						=			{
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "gpt_neox"





   def __init__(       self					:			Optional[int]     , __lowerCamelCase					:			List[str]=50432     , __lowerCamelCase					:			int=6144     , __lowerCamelCase					:			Optional[Any]=44     , __lowerCamelCase					:			Tuple=64     , __lowerCamelCase					:			Optional[int]=24576     , __lowerCamelCase					:			List[str]="gelu"     , __lowerCamelCase					:			Any=0.25     , __lowerCamelCase					:			List[Any]=10000     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			int=0.0     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[Any]=2048     , __lowerCamelCase					:			Tuple=0.02     , __lowerCamelCase					:			Tuple=1e-5     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int=0     , __lowerCamelCase					:			Optional[Any]=2     , __lowerCamelCase					:			List[str]=False     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			Optional[int]=None     , **__lowerCamelCase					:			str     , ):
       super().__init__(bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   rotary_pct
       SCREAMING_SNAKE_CASE										=   rotary_emb_base
       SCREAMING_SNAKE_CASE										=   attention_dropout
       SCREAMING_SNAKE_CASE										=   hidden_dropout
       SCREAMING_SNAKE_CASE										=   classifier_dropout
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps
       SCREAMING_SNAKE_CASE										=   use_cache
       SCREAMING_SNAKE_CASE										=   tie_word_embeddings
       SCREAMING_SNAKE_CASE										=   use_parallel_residual
       SCREAMING_SNAKE_CASE										=   rope_scaling
       self._rope_scaling_validation()

       if self.hidden_size % self.num_attention_heads != 0:
           raise ValueError(
               "The hidden size is not divisble by the number of attention heads! Make sure to update them!"			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling     , __lowerCamelCase			) or len(self.rope_scaling			) != 2:
           raise ValueError(
               "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
               f"got {self.rope_scaling}"			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("type"     , __lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   self.rope_scaling.get("factor"     , __lowerCamelCase			)
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"			)
       if rope_scaling_factor is None or not isinstance(__lowerCamelCase     , __lowerCamelCase			) or rope_scaling_factor <= 1.0:
           raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"			)