




"""simple docstring"""



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 	UpperCAmelCase_  (						A__							):
    lowercase__											=      ['''vqvae''']




    def __init__(    self						:       Dict	, snake_case_						:       AutoencoderKL	, snake_case_						:       UNetaDConditionModel	, snake_case_						:       Mel	, snake_case_						:       Union[DDIMScheduler, DDPMScheduler]	, )      ->							Union[str, Any]:




     '''simple docstring'''

     super().__init__()
     self.register_modules(unet=snake_case_	, scheduler=snake_case_	, mel=snake_case_	, vqvae=snake_case_      )




    def 							__magic_name__							(    self						:       int      )      ->							Tuple:




     '''simple docstring'''

     return 50 if isinstance(self.scheduler	, snake_case_      ) else 1_000




    @torch.no_grad()
    def __call__(    self						:       List[str]	, snake_case_						:       int = 1	, snake_case_						:       str = None	, snake_case_						:       np.ndarray = None	, snake_case_						:       int = 0	, snake_case_						:       int = 0	, snake_case_						:       int = None	, snake_case_						:       torch.Generator = None	, snake_case_						:       float = 0	, snake_case_						:       float = 0	, snake_case_						:       torch.Generator = None	, snake_case_						:       float = 0	, snake_case_						:       torch.Tensor = None	, snake_case_						:       torch.Tensor = None	, snake_case_						:       Union[str, Any]=True	, )      ->							Union[str, Any]:




     '''simple docstring'''

     A__       =				steps or self.get_default_steps()
     self.scheduler.set_timesteps(snake_case_      )
     A__       =				step_generator or generator
     # For backwards compatibility
     if type(self.unet.config.sample_size      ) == int:
      A__       =				(self.unet.config.sample_size, self.unet.config.sample_size)
     if noise is None:
      A__       =				randn_tensor(
          (
              batch_size,
              self.unet.config.in_channels,
              self.unet.config.sample_size[0],
              self.unet.config.sample_size[1],
          )	, generator=snake_case_	, device=self.device	, )
     A__       =				noise
     A__       =				None

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

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

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

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

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

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

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

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

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

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

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




    @torch.no_grad()
    def 							__magic_name__							(    self						:       Dict	, snake_case_						:       List[Image.Image]	, snake_case_						:       int = 50      )      ->							Dict:




     '''simple docstring'''

     assert isinstance(self.scheduler	, snake_case_      )
     self.scheduler.set_timesteps(snake_case_      )
     A__       =				np.array(
         [np.frombuffer(image.tobytes()	, dtype="uint8"      ).reshape((1, image.height, image.width)      ) for image in images]      )
     A__       =				(sample / 255) * 2 - 1
     A__       =				torch.Tensor(snake_case_      ).to(self.device      )

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

     return sample




    @staticmethod
    def 							__magic_name__							(    snake_case_						:       torch.Tensor	, snake_case_						:       torch.Tensor	, snake_case_						:       float      )      ->							Dict:




     '''simple docstring'''

     A__       =				acos(torch.dot(torch.flatten(snake_case_      )	, torch.flatten(snake_case_      )      ) / torch.norm(snake_case_      ) / torch.norm(snake_case_      )      )
     return sin((1 - alpha) * theta      ) * xa / sin(snake_case_      ) + sin(alpha * theta      ) * xa / sin(snake_case_      )













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


A__	:			List[Any]			   = logging.get_logger(__name__)

A__	:			str			   = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A__	:			int			   = {
    'vocab_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
        ),
        'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
        'distilbert-base-uncased-distilled-squad': (
            'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
        'distilbert-base-cased-distilled-squad': (
            'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
        ),
        'distilbert-base-german-cased': (
            'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
        ),
        'distilbert-base-multilingual-cased': (
            'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
    },
}

A__	:			Optional[Any]			   = {
    'distilbert-base-uncased': 5_12,
    'distilbert-base-uncased-distilled-squad': 5_12,
    'distilbert-base-cased': 5_12,
    'distilbert-base-cased-distilled-squad': 5_12,
    'distilbert-base-german-cased': 5_12,
    'distilbert-base-multilingual-cased': 5_12,
}


A__	:			List[str]			   = {
    'distilbert-base-uncased': {'do_lower_case': True},
    'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
    'distilbert-base-cased': {'do_lower_case': False},
    'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
    'distilbert-base-german-cased': {'do_lower_case': False},
    'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       VOCAB_FILES_NAMES
				lowercase__											=       PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=       PRETRAINED_INIT_CONFIGURATION
				lowercase__											=       ["""input_ids""", """attention_mask"""]
				lowercase__											=       DistilBertTokenizer




				def __init__(     self						:    List[Any],  lowerCamelCase						:    List[Any]=None,  lowerCamelCase						:    Dict=None,  lowerCamelCase						:    str=True,  lowerCamelCase						:    Optional[int]="[UNK]",  lowerCamelCase						:    Optional[Any]="[SEP]",  lowerCamelCase						:    List[Any]="[PAD]",  lowerCamelCase						:    Any="[CLS]",  lowerCamelCase						:    Union[str, Any]="[MASK]",  lowerCamelCase						:    str=True,  lowerCamelCase						:    int=None,  **lowerCamelCase						:    Union[str, Any],  ):

								'''simple docstring'''
								super().__init__(
								    lowerCamelCase,  tokenizer_file=lowerCamelCase,  do_lower_case=lowerCamelCase,  unk_token=lowerCamelCase,  sep_token=lowerCamelCase,  pad_token=lowerCamelCase,  cls_token=lowerCamelCase,  mask_token=lowerCamelCase,  tokenize_chinese_chars=lowerCamelCase,  strip_accents=lowerCamelCase,  **lowerCamelCase,  )

								lowercase__    =			json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
								if (
								    normalizer_state.get('''lowercase''',  lowerCamelCase  ) != do_lower_case
								    or normalizer_state.get('''strip_accents''',  lowerCamelCase  ) != strip_accents
								    or normalizer_state.get('''handle_chinese_chars''',  lowerCamelCase  ) != tokenize_chinese_chars
								):
												lowercase__    =			getattr(lowerCamelCase,  normalizer_state.pop('''type'''  )  )
												lowercase__    =			do_lower_case
												lowercase__    =			strip_accents
												lowercase__    =			tokenize_chinese_chars
												lowercase__    =			normalizer_class(**lowerCamelCase  )

								lowercase__    =			do_lower_case




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    List[Any]=None  ):

								'''simple docstring'''
								lowercase__    =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
												output += token_ids_a + [self.sep_token_id]

								return output




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[int],  lowerCamelCase						:    Optional[List[int]] = None  ):

								'''simple docstring'''
								lowercase__    =			[self.sep_token_id]
								lowercase__    =			[self.cls_token_id]
								if token_ids_a is None:
												return len(cls + token_ids_a + sep  ) * [0]
								return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    str,  lowerCamelCase						:    Optional[str] = None  ):

								'''simple docstring'''
								lowercase__    =			self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase  )
								return tuple(lowerCamelCase  )




