



import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						List[Any]						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						Tuple					)      ->			str:
							UpperCAmelCase_=       params

							UpperCAmelCase_=       np.array(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array([len(__UpperCAmelCase					) for t in data]					)

							self.check()
							self.remove_long_sequences()
							self.remove_empty_sequences()
							self.remove_unknown_sequences()
							self.check()
							self.print_statistics()




	def __getitem__(     self     :						List[Any]						,  __UpperCAmelCase     :						int					)      ->			Optional[int]:
							return (self.token_ids[index], self.lengths[index])




	def __len__(     self     :						str					)      ->			Any:
							return len(self.lengths					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							assert len(self.token_ids					) == len(self.lengths					)
							assert all(self.lengths[i] == len(self.token_ids[i]					) for i in range(len(self.lengths					)					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Optional[int]:
							UpperCAmelCase_=       self.params.max_model_input_size
							UpperCAmelCase_=       self.lengths > max_len
							logger.info(F"""Splitting {sum(__UpperCAmelCase					)} too long sequences."""					)

							def divide_chunks(__UpperCAmelCase     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[Any]					):
													return [l[i : i + n] for i in range(0						,  len(__UpperCAmelCase					)						,  __UpperCAmelCase					)]

							UpperCAmelCase_=       []
							UpperCAmelCase_=       []
							if self.params.mlm:
													UpperCAmelCase_,       UpperCAmelCase_=       self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
							else:
													UpperCAmelCase_,       UpperCAmelCase_=       self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]

							for seq_, len_ in zip(self.token_ids						,  self.lengths					):
													assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
													if len_ <= max_len:
																			new_tok_ids.append(seq_					)
																			new_lengths.append(len_					)
													else:
																			UpperCAmelCase_=       []
																			for sub_s in divide_chunks(seq_						,  max_len - 2					):
																									if sub_s[0] != cls_id:
																															UpperCAmelCase_=       np.insert(__UpperCAmelCase						,  0						,  __UpperCAmelCase					)
																									if sub_s[-1] != sep_id:
																															UpperCAmelCase_=       np.insert(__UpperCAmelCase						,  len(__UpperCAmelCase					)						,  __UpperCAmelCase					)
																									assert len(__UpperCAmelCase					) <= max_len
																									assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																									sub_seqs.append(__UpperCAmelCase					)

																			new_tok_ids.extend(__UpperCAmelCase					)
																			new_lengths.extend([len(__UpperCAmelCase					) for l in sub_seqs]					)

							UpperCAmelCase_=       np.array(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Optional[Any]:
							UpperCAmelCase_=       len(self					)
							UpperCAmelCase_=       self.lengths > 11
							UpperCAmelCase_=       self.token_ids[indices]
							UpperCAmelCase_=       self.lengths[indices]
							UpperCAmelCase_=       len(self					)
							logger.info(F"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			str:
							if "unk_token" not in self.params.special_tok_ids:
													return
							else:
													UpperCAmelCase_=       self.params.special_tok_ids["""unk_token"""]
							UpperCAmelCase_=       len(self					)
							UpperCAmelCase_=       np.array([np.count_nonzero(a == unk_token_id					) for a in self.token_ids]					)
							UpperCAmelCase_=       (unk_occs / self.lengths) < 0.5
							UpperCAmelCase_=       self.token_ids[indices]
							UpperCAmelCase_=       self.lengths[indices]
							UpperCAmelCase_=       len(self					)
							logger.info(F"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Tuple:
							if not self.params.is_master:
													return
							logger.info(F"""{len(self					)} sequences"""					)
							# data_len = sum(self.lengths)
							# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
							# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

							# unk_idx = self.params.special_tok_ids['unk_token']
							# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
							# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Optional[int]					)      ->			List[Any]:
							UpperCAmelCase_=       [t[0] for t in batch]
							UpperCAmelCase_=       [t[1] for t in batch]
							assert len(__UpperCAmelCase					) == len(__UpperCAmelCase					)

							# Max for paddings
							UpperCAmelCase_=       max(__UpperCAmelCase					)

							# Pad token ids
							if self.params.mlm:
													UpperCAmelCase_=       self.params.special_tok_ids["""pad_token"""]
							else:
													UpperCAmelCase_=       self.params.special_tok_ids["""unk_token"""]
							UpperCAmelCase_=       [list(t.astype(__UpperCAmelCase					)					) + [pad_idx] * (max_seq_len_ - len(__UpperCAmelCase					)) for t in token_ids]
							assert len(tk_					) == len(__UpperCAmelCase					)
							assert all(len(__UpperCAmelCase					) == max_seq_len_ for t in tk_					)

							UpperCAmelCase_=       torch.tensor(tk_					)  # (bs, max_seq_len_)
							UpperCAmelCase_=       torch.tensor(__UpperCAmelCase					)  # (bs)
							return tk_t, lg_t









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 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: str		       =							["vqvae"]




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 50 if isinstance(self.scheduler						,  __UpperCAmelCase					) else 1_000




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

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

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

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

													UpperCAmelCase_=       (
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													UpperCAmelCase_=       int(mask_start_secs * pixels_per_second					)
													UpperCAmelCase_=       int(mask_end_secs * pixels_per_second					)
													UpperCAmelCase_=       self.scheduler.add_noise(__UpperCAmelCase						,  __UpperCAmelCase						,  torch.tensor(self.scheduler.timesteps[start_step:]					)					)

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

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

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

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

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

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

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




	@torch.no_grad()
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						List[Image.Image]						,  __UpperCAmelCase     :						int = 50					)      ->			np.ndarray:
							assert isinstance(self.scheduler						,  __UpperCAmelCase					)
							self.scheduler.set_timesteps(__UpperCAmelCase					)
							UpperCAmelCase_=       np.array(
							    [np.frombuffer(image.tobytes()						,  dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
							UpperCAmelCase_=       (sample / 255) * 2 - 1
							UpperCAmelCase_=       torch.Tensor(__UpperCAmelCase					).to(self.device					)

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

							return sample




	@staticmethod
	def   _SCREAMING_SNAKE_CASE		(     __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						torch.Tensor						,  __UpperCAmelCase     :						float					)      ->			torch.Tensor:
							UpperCAmelCase_=       acos(torch.dot(torch.flatten(__UpperCAmelCase					)						,  torch.flatten(__UpperCAmelCase					)					) / torch.norm(__UpperCAmelCase					) / torch.norm(__UpperCAmelCase					)					)
							return sin((1 - alpha) * theta					) * xa / sin(__UpperCAmelCase					) + sin(alpha * theta					) * xa / sin(__UpperCAmelCase					)




